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--- |
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license: apache-2.0 |
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task_categories: |
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- tabular-regression |
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- tabular-classification |
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language: |
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- en |
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tags: |
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- e-commerce |
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- customer-analytics |
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- spending-prediction |
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- marketing |
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- retail |
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size_categories: |
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- n<1K |
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--- |
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# E-commerce Customer Spending Dataset |
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[](https://huggingface.co/datasets/Srikanth-Karthi/ecommerce-predictor-data) |
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[](https://www.apache.org/licenses/LICENSE-2.0) |
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[](https://huggingface.co/datasets/Srikanth-Karthi/ecommerce-predictor-data) |
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A dataset containing customer behavior metrics from an e-commerce platform, used to predict yearly spending. |
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## Dataset Description |
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This dataset contains information about customers of an e-commerce company that sells clothing online and also has in-store style sessions. Customers can come to the store for personal styling sessions, then order clothes through a mobile app or website. |
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## Features |
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| Column | Type | Description | |
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|--------|------|-------------| |
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| `Email` | string | Customer email address | |
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| `Address` | string | Customer address | |
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| `Avatar` | string | Avatar color chosen by customer | |
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| `Avg. Session Length` | float | Average in-store session length (minutes) | |
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| `Time on App` | float | Time spent on mobile app (minutes) | |
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| `Time on Website` | float | Time spent on website (minutes) | |
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| `Length of Membership` | float | Years of membership | |
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| `Yearly Amount Spent` | float | Total yearly spending (USD) - **Target Variable** | |
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## Dataset Statistics |
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| Feature | Mean | Std | Min | Max | |
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|---------|------|-----|-----|-----| |
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| Avg. Session Length | 33.05 | 0.99 | 29.53 | 36.14 | |
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| Time on App | 12.05 | 0.99 | 8.51 | 15.13 | |
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| Time on Website | 37.06 | 1.01 | 33.91 | 40.01 | |
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| Length of Membership | 3.53 | 1.00 | 0.27 | 6.92 | |
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| Yearly Amount Spent | 499.31 | 79.31 | 256.67 | 765.52 | |
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## Quick Start |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("Srikanth-Karthi/ecommerce-predictor-data") |
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# View first few rows |
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print(dataset["train"][0]) |
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``` |
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**Or with Pandas:** |
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```python |
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import pandas as pd |
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from huggingface_hub import hf_hub_download |
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# Download and load |
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path = hf_hub_download( |
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repo_id="Srikanth-Karthi/ecommerce-predictor-data", |
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filename="Ecommerce.csv", |
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repo_type="dataset" |
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) |
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df = pd.read_csv(path) |
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print(df.head()) |
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``` |
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## Use Cases |
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- **Regression:** Predict yearly customer spending |
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- **Customer Segmentation:** Cluster customers by behavior |
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- **Feature Analysis:** Understand what drives spending |
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- **Marketing Optimization:** Target high-value customers |
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## Key Insights |
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1. **Length of Membership** has the strongest correlation with yearly spending |
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2. **Time on App** shows higher correlation than **Time on Website** |
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3. Suggests focusing on mobile app development over website improvements |
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## Associated Model |
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This dataset was used to train: |
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- [Srikanth-Karthi/ecommerce-spending-predictor](https://huggingface.co/Srikanth-Karthi/ecommerce-spending-predictor) |
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## Demo |
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Try the live prediction demo: |
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- [Srikanth-Karthi/ecommerce-predictor-demo](https://huggingface.co/spaces/Srikanth-Karthi/ecommerce-predictor-demo) |
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## Citation |
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```bibtex |
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@misc{ecommerce-spending-dataset, |
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author = {Srikanth-Karthi}, |
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title = {E-commerce Customer Spending Dataset}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/datasets/Srikanth-Karthi/ecommerce-predictor-data} |
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} |
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``` |
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